Linear regressive modeling is a technique used to describe systems using a set of linear equations that share common variables, and has a variety of applications in modern communication networks. For instance, linear regressive modeling is used by digital pre-distortion (DPD) circuits to model non-linear characteristics of power amplifiers (PAs). One challenge in linear regressive modeling is to select an appropriate set of coefficients that produces an accurate, yet stable, model of the system. Specifically, if too few coefficients are selected, then the model may become under-defined such that it is incapable of accurately characterizing the system. On the other hand, if too many coefficients are selected, then the model may become over-defined such that it is unstable and/or overly-complex. In a system including a PA and a DPD circuit, an under-defined model may result in poor system linearity (e.g., relatively non-linear relationship between the system's input and output), while an over-defined model may result in an unstable system having a large dynamic range of coefficients. Accordingly, techniques for identifying a set of coefficients that provide tight and stable modeling are desired.